Yeast cells arrest in the G1 phase of the cell cycle upon exposure to mating pheromones. As cells commit to a new cycle, G1 CDK activity (Cln/CDK) inhibits signaling through the mating MAPK cascade. Here we show that the target of this inhibition is Ste5, the MAPK cascade scaffold protein. Cln/CDK disrupts Ste5 membrane localization by phosphorylating a cluster of sites that flank a small, basic, membrane-binding motif in Ste5. Effective inhibition of Ste5 signaling requires multiple phosphorylation sites and a substantial accumulation of negative charge, which suggests that Ste5 acts as a sensor for high G1 CDK activity. Thus, Ste5 is an integration point for both external and internal signals. When Ste5 cannot be phosphorylated, pheromone triggers an aberrant arrest of cells outside G1 either in the presence or absence of the CDK-inhibitor protein Far1. These findings define a mechanism and physiological benefit of restricting antiproliferative signaling to G1.

In platelets, agonists that stimulate phosphoinositide turnover cause the rapid phosphorylation of a protein of apparent relative molecular mass (Mr) 40-47,000, called P47, by protein kinase C (PKC). Diverse identities have been ascribed to P47 including lipocortin, inositol 1,4,5-trisphosphate 5-phosphomonoesterase, pyruvate dehydrogenase alpha subunit and an actin regulatory protein. We have isolated human P47 clones by immunological screening of a lambda gt11 complementary DNA library from HL-60 cells, a human promyelocytic leukaemia cell line. P47 recombinants thus identified hybridized to a 3.0 kilobase (kb) messenger RNA in mature white blood cell lines; the same mRNA was induced in HL-60 cells during differentiation. A 1,050 base pair (bp) open reading frame that could encode a protein of Mr40,087 was confirmed by comparison with peptide sequences from platelet P47, and by expression of the putative recombinant P47 in E. coli and in vitro. The P47 sequence appears to have been conserved throughout vertebrate evolution, and is not similar to any other known sequence including human lipocortin and the alpha subunit of pyruvate dehydrogenase. The P47 protein contains a potential Ca2+-binding 'EF-hand' structure and a region that strongly resembles known PKC phosphorylation sites.

Organisms as diverse as fungi and humans use G-protein-coupled receptors to control signal transduction pathways responsive to various hormones, neuroregulatory molecules and other sensory stimuli. Continual stimulation of these receptors often leads to their desensitization, which is mediated in part by the consecutive actions of two families of proteins--the G-protein-coupled receptor kinases, which phosphorylate the agonist-occupied receptors, and the arrestin proteins, which subsequently bind to the receptors. We now present evidence that a group of proteins--the G0S8/Sst2p family--may be a third class of receptor-desensitizing factors.

Genome integrity is jeopardized each time DNA replication forks stall or collapse. Here we report the identification of a complex composed of MMS22L (C6ORF167) and TONSL (NFKBIL2) that participates in the recovery from replication stress. MMS22L and TONSL are homologous to yeast Mms22 and plant Tonsoku/Brushy1, respectively. MMS22L-TONSL accumulates at regions of ssDNA associated with distressed replication forks or at processed DNA breaks, and its depletion results in high levels of endogenous DNA double-strand breaks caused by an inability to complete DNA synthesis after replication fork collapse. Moreover, cells depleted of MMS22L are highly sensitive to camptothecin, a topoisomerase I poison that impairs DNA replication progression. Finally, MMS22L and TONSL are necessary for the efficient formation of RAD51 foci after DNA damage, and their depletion impairs homologous recombination. These results indicate that MMS22L and TONSL are genome caretakers that stimulate the recombination-dependent repair of stalled or collapsed replication forks.

BackgroundThe identification of genomic biomarkers is a key step towards improving diagnostic tests and therapies. We present a reference-free method for this task that relies on a k-mer representation of genomes and a machine learning algorithm that produces intelligible models. The method is computationally scalable and well-suited for whole genome sequencing studies.ResultsThe method was validated by generating models that predict the antibiotic resistance of C. difficile, M. tuberculosis, P. aeruginosa, and S. pneumoniae for 17 antibiotics. The obtained models are accurate, faithful to the biological pathways targeted by the antibiotics, and they provide insight into the process of resistance acquisition. Moreover, a theoretical analysis of the method revealed tight statistical guarantees on the accuracy of the obtained models, supporting its relevance for genomic biomarker discovery.ConclusionsOur method allows the generation of accurate and interpretable predictive models of phenotypes, which rely on a small set of genomic variations. The method is not limited to predicting antibiotic resistance in bacteria and is applicable to a variety of organisms and phenotypes. Kover, an efficient implementation of our method, is open-source and should guide biological efforts to understand a plethora of phenotypes (http://github.com/aldro61/kover/).Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2889-6) contains supplementary material, which is available to authorized users.

Background:The identification of genomic biomarkers is a key step towards improving diagnostic tests and therapies. We present a reference-free method for this task that relies on a k-mer representation of genomes and a machine learning algorithm that produces intelligible models. The method is computationally scalable and well-suited for whole genome sequencing studies. Results: The method was validated by generating models that predict the antibiotic resistance of C. difficile, M. tuberculosis, P. aeruginosa, and S. pneumoniae for 17 antibiotics. The obtained models are accurate, faithful to the biological pathways targeted by the antibiotics, and they provide insight into the process of resistance acquisition. Moreover, a theoretical analysis of the method revealed tight statistical guarantees on the accuracy of the obtained models, supporting its relevance for genomic biomarker discovery. Conclusions: Our method allows the generation of accurate and interpretable predictive models of phenotypes, which rely on a small set of genomic variations. The method is not limited to predicting antibiotic resistance in bacteria and is applicable to a variety of organisms and phenotypes. Kover, an efficient implementation of our method, is open-source and should guide biological efforts to understand a plethora of phenotypes (http://github. com/aldro61/kover/).

Activation of protein kinase C (PKC) in platelets causes the immediate phosphorylation of pleckstrin, an apparent Mr 40-47,000 protein previously called 40K or P47. Pleckstrin presumably plays an important but as yet unknown role in mediating cellular responses evoked by agonist-induced phosphoinositide turnover. We have cloned the cDNA for pleckstrin from the HL-60 human promyelocytic leukemia cell line by immunological screening of a lambda gt11 expression library (Tyers et al.: Nature 333:470-473, 1988) and now report further analysis of the pleckstrin sequence. Pleckstrin has a deduced Mr of 40,087 and is encoded by a 1,050-bp open reading frame which is preceded by a short open reading frame that terminates before the correct initiator methionine. A single polymorphic site was found in the coding region. An unusual pattern of sequence heterogeneity occurred about a poly(A) tract in the 3' untranslated region. The 3.0-kb pleckstrin mRNA induced upon differentiation of HL-60 cells apparently has heterogeneous 5' ends which undergo differential regulation during HL-60 cell maturation. Analysis by multiple sequence alignment with known PKC substrates identified a strong candidate site for phosphorylation by PKC and a potential Ca2+-binding EF-hand motif. No other similarities to proteins in current databases were found.

SUMMARY It is increasingly appreciated that alternative splicing plays a key role in generating functional specificity and diversity in cancer. However, the mechanisms by which cancer mutations perturb splicing remain unknown. Here, we developed a network-based strategy, DrAS-Net, to investigate over 2.5 million variants across cancer types and link somatic mutations with cancer-specific splicing events. We identified over 40,000 driver variant candidates and their 80,000 putative splicing targets deregulated in 33 cancer types and inferred their functional impact. Strikingly, tumors with splicing perturbations show reduced expression of immune system-related genes, and increased expression of cell proliferation markers. Tumors harboring different mutations in the same gene often exhibit distinct splicing perturbations. Further stratification of 10,000 patients based on their mutation-splicing relationships identifies subtypes with distinct clinical features, including survival rates. Our work reveals how single nucleotide changes can alter the repertoires of splicing isoforms, providing insights into oncogenic mechanisms for precision medicine.